Pacotes

library(tidyverse)
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## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
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library(geobr)
## Loading required namespace: sf
library(sf)
## Linking to GEOS 3.11.2, GDAL 3.6.2, PROJ 9.2.0; sf_use_s2() is TRUE
library(terra)
## terra 1.7.29
## 
## Attaching package: 'terra'
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library(tidyterra)
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## Attaching package: 'tidyterra'
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##     filter
library(ggnewscale)

library(patchwork)
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## Attaching package: 'patchwork'
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library(cowplot)
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## Attaching package: 'cowplot'
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Dados

Shappefiles

Saltinho

saltinho <- sf::st_read("saltinho.shp")
## Reading layer `saltinho' from data source 
##   `G:\Meu Drive\UFPE\projeto mestrado\mestrado\saltinho.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 14 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -35.19784 ymin: -8.739185 xmax: -35.16593 ymax: -8.712074
## Geodetic CRS:  SIRGAS 2000
saltinho
## Simple feature collection with 1 feature and 14 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -35.19784 ymin: -8.739185 xmax: -35.16593 ymax: -8.712074
## Geodetic CRS:  SIRGAS 2000
##   cd_cns_                       nm_cns_ id_wcm           categry group gvrnmn_
## 1     198 RESERVA BIOLÓGICA DE SALTINHO   6957 Reserva Biológica    PI federal
##   crtn_yr gid7
## 1    1983  300
##                                                                            quality
## 1 Correto (O poligono corresponde ao memorial descritivo do ato legal de criação).
##                          legsltn    dt_lt10      cod_111
## 1 Decreto nÂș 88744 de 21/09/1983 30/11/2007 0000.00.0198
##                                                   nm_rgnz   date
## 1 Instituto Chico Mendes de Conservação da Biodiversidade 201909
##                         geometry
## 1 POLYGON ((-35.17557 -8.7146...
saltinho %>% 
  ggplot() +
  geom_sf()

Estados

estados <- geobr::read_state(showProgress = FALSE) 
## Using year 2010
estados
## Simple feature collection with 27 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -66.81025 ymin: -13.6937 xmax: -59.77435 ymax: -7.969294
## Geodetic CRS:  SIRGAS 2000
## First 10 features:
##    code_state abbrev_state name_state code_region name_region
## 1          11           RO   RondĂŽnia           1       Norte
## 2          12           AC       Acre           1       Norte
## 3          13           AM   Amazonas           1       Norte
## 4          14           RR    Roraima           1       Norte
## 5          15           PA       ParĂĄ           1       Norte
## 6          16           AP      AmapĂĄ           1       Norte
## 7          17           TO  Tocantins           1       Norte
## 8          21           MA   MaranhĂŁo           2    Nordeste
## 9          22           PI      PiauĂ­           2    Nordeste
## 10         23           CE      CearĂĄ           2    Nordeste
##                              geom
## 1  MULTIPOLYGON (((-63.32721 -...
## 2  MULTIPOLYGON (((-73.18253 -...
## 3  MULTIPOLYGON (((-67.32609 2...
## 4  MULTIPOLYGON (((-60.20051 5...
## 5  MULTIPOLYGON (((-54.95431 2...
## 6  MULTIPOLYGON (((-51.1797 4....
## 7  MULTIPOLYGON (((-48.35878 -...
## 8  MULTIPOLYGON (((-45.84073 -...
## 9  MULTIPOLYGON (((-41.74605 -...
## 10 MULTIPOLYGON (((-41.16703 -...
estados %>% 
  ggplot() +
  geom_sf()

Continentes

continentes <- sf::st_read("World_Continents.shp")
## Reading layer `World_Continents' from data source 
##   `G:\Meu Drive\UFPE\projeto mestrado\mestrado\World_Continents.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 8 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -89 xmax: 180 ymax: 83.6236
## Geodetic CRS:  WGS 84
continentes
## Simple feature collection with 8 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -89 xmax: 180 ymax: 83.6236
## Geodetic CRS:  WGS 84
##   FID     CONTINENT       SQMI       SQKM   Shape__Are Shape__Len
## 1   1        Africa 11583462.7 30001150.8 3.353511e+13   49144798
## 2   2          Asia 17317280.1 44851729.0 1.145290e+14  311176819
## 3   3     Australia  2973612.2  7701651.1 9.652152e+12   29969539
## 4   4 North America  9339528.5 24189364.5 1.113144e+14  595152476
## 5   5       Oceania   165678.7   429107.6 6.581670e+11   26177519
## 6   6 South America  6856255.3 17757690.9 2.068439e+13   77372912
## 7   7    Antarctica  4754809.5 12314949.2 6.966421e+14  253068489
## 8   8        Europe  3821854.3  9898596.9 3.508924e+13  236911733
##                         geometry
## 1 MULTIPOLYGON (((35.48832 -2...
## 2 MULTIPOLYGON (((-180 68.980...
## 3 MULTIPOLYGON (((158.8822 -5...
## 4 MULTIPOLYGON (((-81.67847 7...
## 5 MULTIPOLYGON (((180 -16.965...
## 6 MULTIPOLYGON (((-67.20889 -...
## 7 MULTIPOLYGON (((-180 -84.30...
## 8 MULTIPOLYGON (((23.84853 35...
continentes %>% 
  ggplot() +
  geom_sf()

Trilhas

trilhas <- sf::st_read("Trilhas.shp")
## Reading layer `Trilhas' from data source 
##   `G:\Meu Drive\UFPE\projeto mestrado\mestrado\Trilhas.shp' using driver `ESRI Shapefile'
## Simple feature collection with 4 features and 11 fields
## Geometry type: LINESTRING
## Dimension:     XYZ
## Bounding box:  xmin: -35.19158 ymin: -8.737647 xmax: -35.16856 ymax: -8.715277
## z_range:       zmin: 0 zmax: 0
## Geodetic CRS:  WGS 84
trilhas
## Simple feature collection with 4 features and 11 fields
## Geometry type: LINESTRING
## Dimension:     XYZ
## Bounding box:  xmin: -35.19158 ymin: -8.737647 xmax: -35.16856 ymax: -8.715277
## z_range:       zmin: 0 zmax: 0
## Geodetic CRS:  WGS 84
##       Name descriptio timestamp begin  end altitudeMo tessellate extrude
## 1 Trilha 1       <NA>      <NA>  <NA> <NA>       <NA>          1       0
## 2 Trilha 2       <NA>      <NA>  <NA> <NA>       <NA>          1       0
## 3 Trilha 3       <NA>      <NA>  <NA> <NA>       <NA>          1       0
## 4 Trilha 4       <NA>      <NA>  <NA> <NA>       <NA>          1       0
##   visibility drawOrder icon                       geometry
## 1         -1        NA <NA> LINESTRING Z (-35.18349 -8....
## 2         -1        NA <NA> LINESTRING Z (-35.19158 -8....
## 3         -1        NA <NA> LINESTRING Z (-35.18818 -8....
## 4         -1        NA <NA> LINESTRING Z (-35.17173 -8....
trilhas %>% 
  ggplot() +
  geom_sf(aes(color = Name)) +
  labs(color = NULL)

Parcelas

parcelas <- sf::st_read("Parcelas.shp")
## Reading layer `Parcelas' from data source 
##   `G:\Meu Drive\UFPE\projeto mestrado\mestrado\Parcelas.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 16 features and 11 fields
## Geometry type: LINESTRING
## Dimension:     XYZ
## Bounding box:  xmin: -35.19158 ymin: -8.737648 xmax: -35.16721 ymax: -8.714365
## z_range:       zmin: 0 zmax: 0
## Geodetic CRS:  WGS 84
parcelas
## Simple feature collection with 16 features and 11 fields
## Geometry type: LINESTRING
## Dimension:     XYZ
## Bounding box:  xmin: -35.19158 ymin: -8.737648 xmax: -35.16721 ymax: -8.714365
## z_range:       zmin: 0 zmax: 0
## Geodetic CRS:  WGS 84
## First 10 features:
##                      Name descriptio timestamp begin  end altitudeMo tessellate
## 1  Transecto 1 - trilha 1       <NA>      <NA>  <NA> <NA>       <NA>          1
## 2  Transecto 2 - trilha 1       <NA>      <NA>  <NA> <NA>       <NA>          1
## 3  Transecto 3 - trilha 1       <NA>      <NA>  <NA> <NA>       <NA>          1
## 4  Transecto 4 - trilha 1       <NA>      <NA>  <NA> <NA>       <NA>          1
## 5  Transecto 1 - trilha 2       <NA>      <NA>  <NA> <NA>       <NA>          1
## 6  Transecto 2 - trilha 2       <NA>      <NA>  <NA> <NA>       <NA>          1
## 7  Transecto 3 - trilha 2       <NA>      <NA>  <NA> <NA>       <NA>          1
## 8  Transecto 4 - trilha 2       <NA>      <NA>  <NA> <NA>       <NA>          1
## 9  Transecto 5 - trilha 2       <NA>      <NA>  <NA> <NA>       <NA>          1
## 10 Transecto 6 - trilha 2       <NA>      <NA>  <NA> <NA>       <NA>          1
##    extrude visibility drawOrder icon                       geometry
## 1        0         -1        NA <NA> LINESTRING Z (-35.1734 -8.7...
## 2        0         -1        NA <NA> LINESTRING Z (-35.17659 -8....
## 3        0         -1        NA <NA> LINESTRING Z (-35.18014 -8....
## 4        0         -1        NA <NA> LINESTRING Z (-35.18349 -8....
## 5        0         -1        NA <NA> LINESTRING Z (-35.17363 -8....
## 6        0         -1        NA <NA> LINESTRING Z (-35.17716 -8....
## 7        0         -1        NA <NA> LINESTRING Z (-35.18082 -8....
## 8        0         -1        NA <NA> LINESTRING Z (-35.18437 -8....
## 9        0         -1        NA <NA> LINESTRING Z (-35.18797 -8....
## 10       0         -1        NA <NA> LINESTRING Z (-35.19158 -8....
parcelas %>% 
  ggplot() +
  geom_sf(aes(color = Name)) +
  labs(color = NULL)

Imagem de satélite

saltinho_sat <- terra::rast("Saltinho.tif")

saltinho_sat
## class       : SpatRaster 
## dimensions  : 6225, 8442, 4  (nrow, ncol, nlyr)
## resolution  : 5.475979e-06, 5.475979e-06  (x, y)
## extent      : -35.20319, -35.15696, -8.744113, -8.710025  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat SIRGAS 2000 (EPSG:4674) 
## source      : Saltinho.tif 
## names       : Saltinho_1, Saltinho_2, Saltinho_3, Saltinho_4
ggplot() +
  tidyterra::geom_spatraster_rgb(data = saltinho_sat)
## SpatRaster resampled to ncells = 500992

Mapa

Mapa do Brasil-América do Sul

br <- ggplot()+
  geom_sf(data = continentes, color = "black", fill = "gray40") +
  geom_sf(data = estados, color = "black", fill = "white") +
  geom_sf(data = estados %>% dplyr::filter(name_state == "Pernambuco"), fill = "gray", color = "black") +
  geom_rect(aes(xmin = -41.25, xmax = -34.9, ymin = -9.5, ymax = -7.25, color = "Pernambuco"), fill = "red", alpha = 0.4) +
  scale_color_manual(values = "red") +
  coord_sf(xlim = c(-80, -35), ylim = c(-55, 12)) +
  labs(color = NULL,
       fill = NULL) +
  theme_classic() +
  theme(legend.position = "bottom",
        axis.text = element_text(color = "black"),
        plot.background = element_blank(),
        panel.background = element_blank(),
        plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"))

br

Mapa de Pernambuco

gg_pe <- ggplot() +
  geom_sf(data = estados, color = "black", fill = "white") +
  geom_sf(data = estados %>% dplyr::filter(name_state == "Pernambuco"), aes(fill = "Pernambuco"), color = "black") +
  tidyterra::geom_spatraster_rgb(data = saltinho_sat) +
  geom_rect(aes(xmin = -35.20, xmax = -35.16, ymin = -8.7426, ymax = -8.712, color = "REBio Saltinho"), fill = "red", alpha = 0.4) +
  coord_sf(xlim = c(-41.25, -34.9), ylim = c(-9.5, -7.25)) +
  labs(color = NULL,
       fill = NULL) +
  scale_color_manual(values = "red") +
  scale_fill_manual(values = "gray") +
  ggspatial::annotation_scale(location = "br", height = unit(0.3,"cm"), bar_cols = c("black", "white")) +
  theme_classic() +
  theme(legend.position = "bottom",
        axis.text = element_text(color = "black"),
        plot.background = element_blank(),
        panel.background = element_blank(),
        plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"))
## SpatRaster resampled to ncells = 500992
gg_pe

Mapa de Saltinho

Transformando os dados das trilhas e das parcelas em dataframe

trilhas_df <- trilhas %>% sf::st_coordinates() %>% 
  as.data.frame() %>% 
  dplyr::mutate(Trilha = L1 %>% as.character(),
                Trilha = paste0("Trilha ", Trilha)) %>% 
  dplyr::select(c(1:2, 5))

parcelas_df <- parcelas %>% sf::st_coordinates() %>% 
  as.data.frame() %>% 
  dplyr::mutate(Parcela = L1 %>% as.character())

trilhas_df
##            X         Y   Trilha
## 1  -35.18349 -8.715277 Trilha 1
## 2  -35.18014 -8.718307 Trilha 1
## 3  -35.17658 -8.721506 Trilha 1
## 4  -35.17340 -8.724380 Trilha 1
## 5  -35.19158 -8.716887 Trilha 2
## 6  -35.18798 -8.719664 Trilha 2
## 7  -35.18437 -8.722436 Trilha 2
## 8  -35.18082 -8.725163 Trilha 2
## 9  -35.17716 -8.727953 Trilha 2
## 10 -35.17362 -8.730673 Trilha 2
## 11 -35.18818 -8.725463 Trilha 3
## 12 -35.18454 -8.728118 Trilha 3
## 13 -35.18081 -8.730817 Trilha 3
## 14 -35.17722 -8.733469 Trilha 3
## 15 -35.17173 -8.734406 Trilha 4
## 16 -35.16856 -8.737647 Trilha 4
parcelas_df
##             X         Y Z L1 Parcela
## 1   -35.17340 -8.724380 0  1       1
## 2   -35.17343 -8.724467 0  1       1
## 3   -35.17350 -8.724516 0  1       1
## 4   -35.17352 -8.724606 0  1       1
## 5   -35.17357 -8.724681 0  1       1
## 6   -35.17354 -8.724766 0  1       1
## 7   -35.17354 -8.724856 0  1       1
## 8   -35.17361 -8.724915 0  1       1
## 9   -35.17370 -8.724891 0  1       1
## 10  -35.17377 -8.724835 0  1       1
## 11  -35.17381 -8.724916 0  1       1
## 12  -35.17382 -8.725007 0  1       1
## 13  -35.17386 -8.725083 0  1       1
## 14  -35.17395 -8.725118 0  1       1
## 15  -35.17397 -8.725202 0  1       1
## 16  -35.17391 -8.725266 0  1       1
## 17  -35.17389 -8.725357 0  1       1
## 18  -35.17388 -8.725443 0  1       1
## 19  -35.17392 -8.725522 0  1       1
## 20  -35.17401 -8.725546 0  1       1
## 21  -35.17409 -8.725500 0  1       1
## 22  -35.17418 -8.725477 0  1       1
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## 412 -35.17028 -8.733376 0 16      16
## 413 -35.17020 -8.733338 0 16      16
## 414 -35.17012 -8.733306 0 16      16

Mapa

salt <- ggplot() +
  geom_spatraster_rgb(data = saltinho_sat) +
  geom_sf(data = saltinho, aes(color = "REBio Saltinho"), fill = NA, linewidth = 1.5) +
  coord_sf(xlim = c(-35.20, -35.16), ylim = c(-8.7426, -8.712)) +
  scale_color_manual(values = "red") +
  labs(color = NULL) +
  ggnewscale::new_scale_colour() +
  geom_line(data = trilhas_df, aes(X, Y, color = Trilha), linewidth = 1.25, show.legend = FALSE) +
  scale_color_manual(values = rep("yellow", 4)) +
  labs(color = NULL,
       x = NULL, 
       y = NULL) +
  ggnewscale::new_scale_colour() +
  geom_line(data = parcelas_df, aes(X, Y, color = Parcela), show.legend = FALSE) +
  scale_color_manual(values = rep("yellow", 16)) +
  labs(color = NULL,
       x = NULL, 
       y = NULL) +
  guides(color = guide_legend(order = 2)) +
  ggspatial::annotation_scale(location = "br", height = unit(0.3,"cm"), bar_cols = c("black", "white")) +
  theme_classic() +
  theme(legend.position = "bottom",
        axis.text = element_text(color = "black"),
        plot.background = element_blank(),
        panel.background = element_blank(),
        plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"))
## SpatRaster resampled to ncells = 500992
salt

Unificando os mapas

Estado de Pernambuco + Saltinho

mapa_1 <- gg_pe + salt + patchwork::plot_layout(nrow = 2)

mapa_1

Fundo de para o mapa final

df <- data.frame(x = 1, y = 1)

gg <- df %>% 
  ggplot(aes(x, y)) +
  theme(panel.background = element_rect(color = "white", fill = "white"),
        plot.background = element_rect(color = "white", fill = "white"),
        axis.title = element_text(color = "white"),
        axis.text = element_text(color = "white"))

gg 

ggsave(filename = "vazio.png")
## Saving 10 x 8 in image

Mapa final

gg +
  draw_plot(mapa_1, width = 0.5, height = 1, x = 0, y = 0) +  
  draw_plot(br, width = 0.425, height = 0.925, x = 0.48, y = 0.05)

ggsave(filename = "saltinho_mapa_trilhas.png", height = 12, widt = 14)

Salvando as coordenadas em bases de dados

Trilhas

trilhas_xlsx <- trilhas %>% 
  sf::st_coordinates() %>% 
  as.data.frame() %>% 
  dplyr::mutate(Longitude = X,
                Latitude = Y,
                Trilha = L1 %>% as.character(),
                Ponto = c(seq(1:4) %>% sort(decreasing = TRUE),
                          seq(1:6) %>% sort(decreasing = TRUE),
                          seq(1:4) %>% sort(decreasing = TRUE),
                          seq(1:2) %>% sort(decreasing = TRUE))) %>% 
  dplyr::select(5:8) %>% 
  dplyr::arrange(Trilha, Ponto) 

trilhas_xlsx
##    Longitude  Latitude Trilha Ponto
## 1  -35.17340 -8.724380      1     1
## 2  -35.17658 -8.721506      1     2
## 3  -35.18014 -8.718307      1     3
## 4  -35.18349 -8.715277      1     4
## 5  -35.17362 -8.730673      2     1
## 6  -35.17716 -8.727953      2     2
## 7  -35.18082 -8.725163      2     3
## 8  -35.18437 -8.722436      2     4
## 9  -35.18798 -8.719664      2     5
## 10 -35.19158 -8.716887      2     6
## 11 -35.17722 -8.733469      3     1
## 12 -35.18081 -8.730817      3     2
## 13 -35.18454 -8.728118      3     3
## 14 -35.18818 -8.725463      3     4
## 15 -35.16856 -8.737647      4     1
## 16 -35.17173 -8.734406      4     2
trilhas_xlsx %>% openxlsx::write.xlsx("trilhas.xlsx")

Parcelas

parcelas_xlsx <- parcelas %>% 
  sf::st_coordinates() %>% 
  as.data.frame() %>% 
  dplyr::mutate(Longitude = X,
                Latitude = Y,
                Parcela = L1 %>% as.character(),
                Trilha = c(rep("1", 103),
                           rep("2", 156),
                           rep("3", 103),
                           rep("4", 52))) %>% 
  dplyr::select(5:8) 

parcelas_xlsx
##     Longitude  Latitude Parcela Trilha
## 1   -35.17340 -8.724380       1      1
## 2   -35.17343 -8.724467       1      1
## 3   -35.17350 -8.724516       1      1
## 4   -35.17352 -8.724606       1      1
## 5   -35.17357 -8.724681       1      1
## 6   -35.17354 -8.724766       1      1
## 7   -35.17354 -8.724856       1      1
## 8   -35.17361 -8.724915       1      1
## 9   -35.17370 -8.724891       1      1
## 10  -35.17377 -8.724835       1      1
## 11  -35.17381 -8.724916       1      1
## 12  -35.17382 -8.725007       1      1
## 13  -35.17386 -8.725083       1      1
## 14  -35.17395 -8.725118       1      1
## 15  -35.17397 -8.725202       1      1
## 16  -35.17391 -8.725266       1      1
## 17  -35.17389 -8.725357       1      1
## 18  -35.17388 -8.725443       1      1
## 19  -35.17392 -8.725522       1      1
## 20  -35.17401 -8.725546       1      1
## 21  -35.17409 -8.725500       1      1
## 22  -35.17418 -8.725477       1      1
## 23  -35.17426 -8.725516       1      1
## 24  -35.17430 -8.725593       1      1
## 25  -35.17427 -8.725680       1      1
## 26  -35.17421 -8.725754       1      1
## 27  -35.17659 -8.721500       2      1
## 28  -35.17653 -8.721435       2      1
## 29  -35.17653 -8.721345       2      1
## 30  -35.17652 -8.721255       2      1
## 31  -35.17644 -8.721206       2      1
## 32  -35.17635 -8.721214       2      1
## 33  -35.17627 -8.721172       2      1
## 34  -35.17618 -8.721149       2      1
## 35  -35.17614 -8.721066       2      1
## 36  -35.17609 -8.720993       2      1
## 37  -35.17600 -8.720955       2      1
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## 39  -35.17590 -8.720814       2      1
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## 48  -35.17540 -8.720770       2      1
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## 50  -35.17540 -8.720940       2      1
## 51  -35.17547 -8.721003       2      1
## 52  -35.18014 -8.718304       3      1
## 53  -35.18012 -8.718214       3      1
## 54  -35.18014 -8.718126       3      1
## 55  -35.18011 -8.718041       3      1
## 56  -35.18002 -8.718063       3      1
## 57  -35.17998 -8.717980       3      1
## 58  -35.17999 -8.717891       3      1
## 59  -35.17995 -8.717808       3      1
## 60  -35.17987 -8.717833       3      1
## 61  -35.17977 -8.717826       3      1
## 62  -35.17970 -8.717775       3      1
## 63  -35.17969 -8.717683       3      1
## 64  -35.17971 -8.717594       3      1
## 65  -35.17972 -8.717512       3      1
## 66  -35.17972 -8.717413       3      1
## 67  -35.17969 -8.717332       3      1
## 68  -35.17960 -8.717325       3      1
## 69  -35.17957 -8.717235       3      1
## 70  -35.17949 -8.717240       3      1
## 71  -35.17947 -8.717149       3      1
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## 412 -35.17028 -8.733376      16      4
## 413 -35.17020 -8.733338      16      4
## 414 -35.17012 -8.733306      16      4
parcelas_xlsx %>% openxlsx::write.xlsx("parcelas.xlsx")